scFlow - Merged Multi-Sample Quality-Control Report

Sample Pseudobulking Plots

Pseudobulked Sample Dimensionality Reduction Plots

These plots represent the pseudobulking of all cells from a sample (“manifest”) into a single point in two-dimensional space. Taken together with the merge summary plots, these may help to identify potential outliers or otherwise problematic samples. Data are visualized with: PCA, UMAP_PCA.

## Warning: Use of `dt$dim1` is discouraged. Use `dim1` instead.

## Warning: Use of `dt$dim2` is discouraged. Use `dim2` instead.

## Warning: Use of `dt$feature_dim` is discouraged. Use `feature_dim` instead.

PCA

## Warning: Use of `dt$dim1` is discouraged. Use `dim1` instead.

## Warning: Use of `dt$dim2` is discouraged. Use `dim2` instead.

## Warning: Use of `dt$feature_dim` is discouraged. Use `feature_dim` instead.

UMAP_PCA

Pseudobulked Sample Gene Expressivity Plot

Below, the pseudobulked counts matrix is used to determine which genes are detected or are absent in each sample. Hierarchical clustering with Euclidean distance is used to visualize the samples according to these binarized gene expressivity matrices. Samples with marked differences in the number and range of expressed genes may be potentially problematic and this facet of quality-control may be further explored in the related total_features_by_counts plots in this report.

<b>Figure: Heatmap and dendrogram of gene expressivity by sample.</b> Genes (rows) and samples (columns) are hierarchically clustered and reveal the number and distribution of expressed (purple) and non-expressed (yellow) genes per sample.

Figure: Heatmap and dendrogram of gene expressivity by sample. Genes (rows) and samples (columns) are hierarchically clustered and reveal the number and distribution of expressed (purple) and non-expressed (yellow) genes per sample.

Multi-Sample Summary Plots

The multi-sample summary plots and matching data tables below examine key sample quality metrics (total_features_by_counts, total_counts, pc_mito, pc_ribo) across all experimental samples. These highlight samples with marked differences in these metrics and may be used to inform revision of quality-control thresholds. In more extreme cases (e.g. severe outliers, sample degradation, etc.), it may be appropriate to omit sample(s) from an analysis. To facilitate the identification of these potentially problematic samples, the QC column in the tables below highlight any samples with metrics falling outside of two or three standard deviations (σ) of the mean.

These metrics are also visualized across the specified facet variables: group , diagnosis , seqdate , PMI , RIN .

total_features_by_counts

Figure: total_features_by_counts by individual sample (manifest).

Table: total_features_by_counts by individual sample (manifest).

total_features_by_counts_vs_group

Figure: total_features_by_counts by group.

Table: total_features_by_counts by group.

total_features_by_counts_vs_diagnosis

Figure: total_features_by_counts by diagnosis.

Table: total_features_by_counts by diagnosis.

total_features_by_counts_vs_seqdate

Figure: total_features_by_counts by seqdate.

Table: total_features_by_counts by seqdate.

total_features_by_counts_vs_PMI

Figure: total_features_by_counts by PMI.

Table: total_features_by_counts by PMI.

total_features_by_counts_vs_RIN

Figure: total_features_by_counts by RIN.

Table: total_features_by_counts by RIN.

total_counts

Figure: total_counts by individual sample (manifest).

Table: total_counts by individual sample (manifest).

total_counts_vs_group

Figure: total_counts by group.

Table: total_counts by group.

total_counts_vs_diagnosis

Figure: total_counts by diagnosis.

Table: total_counts by diagnosis.

total_counts_vs_seqdate

Figure: total_counts by seqdate.

Table: total_counts by seqdate.

total_counts_vs_PMI

Figure: total_counts by PMI.

Table: total_counts by PMI.

total_counts_vs_RIN

Figure: total_counts by RIN.

Table: total_counts by RIN.

pc_mito

Figure: pc_mito by individual sample (manifest).

Table: pc_mito by individual sample (manifest).

pc_mito_vs_group

Figure: pc_mito by group.

Table: pc_mito by group.

pc_mito_vs_diagnosis

Figure: pc_mito by diagnosis.

Table: pc_mito by diagnosis.

pc_mito_vs_seqdate

Figure: pc_mito by seqdate.

Table: pc_mito by seqdate.

pc_mito_vs_PMI

Figure: pc_mito by PMI.

Table: pc_mito by PMI.

pc_mito_vs_RIN

Figure: pc_mito by RIN.

Table: pc_mito by RIN.

pc_ribo

Figure: pc_ribo by individual sample (manifest).

Table: pc_ribo by individual sample (manifest).

pc_ribo_vs_group

Figure: pc_ribo by group.

Table: pc_ribo by group.

pc_ribo_vs_diagnosis

Figure: pc_ribo by diagnosis.

Table: pc_ribo by diagnosis.

pc_ribo_vs_seqdate

Figure: pc_ribo by seqdate.

Table: pc_ribo by seqdate.

pc_ribo_vs_PMI

Figure: pc_ribo by PMI.

Table: pc_ribo by PMI.

pc_ribo_vs_RIN

Figure: pc_ribo by RIN.

Table: pc_ribo by RIN.

scFlow v0.4.5 – 2020-03-10 20:12:52

A report by scFlow